International aid may take the form of multilateral aid – provided through international bodies such as the UN, or NGOs such as Oxfam – or bilateral aid, which operates on a government-to-government basis. There is considerable debate about whether international aid works, in the sense of reducing poverty and stimulating development.
However, the effectiveness of aid is often diluted by corruption. Aid is invariably channeled through the governments of recipient countries, in which power is often concentrated in the hands of a few politicians and bureaucrats, and the mechanisms of accountability are, at best, poorly developed. This tends to benefit corrupt leaders and elites rather than the people, projects and programs for which it was intended.
Watts, Carl. (2014). Re: Does foreign aid help the developing countries towards development?. Retrieved from: https://www.researchgate.net/post/Does_foreign_aid_help_the_developing_countries_towards_development/5322005ed039b1e7648b459c/citation/download.
The hypothesis that foreign aid can promote growth in developing countries was explored, using panel data series for foreign aid, while accounting for regional differences in Asian, African, Latin American, and the Caribbean countries as well as the differences in income levels, the results of this study also indicate that foreign aid has mixed effects on economic growth in developing countries.
Ekanayake, E. & Chatrna, Dasha. (2010). The effect of foreign aid on economic growth in developing countries. Journal of International Business and Cultural Studies. 3.
This study examines the relationships between foreign aid, institutional structure, and economic performance for 80 countries in Europe, America, Africa, and Asia. It is found that official development assistance and the quality of institutional structure in the sample countries affect economic growth positively.
Hayaloğlu, Pınar. (2023). Foreign Aid, Institutions, and Economic Performance in Developing Countries. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 18. 748-765. 10.17153/oguiibf.1277348.
Algunas librerias y paquetes usados para obtener y descargar los datos
library(tidyverse) # manejo de dataframes
Warning: replacing previous import ‘lifecycle::last_warnings’ by ‘rlang::last_warnings’ when loading ‘pillar’Warning: replacing previous import ‘lifecycle::last_warnings’ by ‘rlang::last_warnings’ when loading ‘tibble’Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
Warning: replacing previous import ‘lifecycle::last_warnings’ by ‘rlang::last_warnings’ when loading ‘hms’── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.2 ✓ dplyr 1.0.7
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 2.0.1 ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
library(reshape2) # para tranfromar data de long a wide
Attaching package: ‘reshape2’
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smiths
library(WDI) # libreria para acceder a metadata de banco mundial
library(readxl) # leer archivos de excel
library(readr) # leer archivos csv
library(visdat) # visualizacion de datos como graficos
library(plotly) # graficos
Registered S3 method overwritten by 'data.table':
method from
print.data.table
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method from
print.htmlwidget tools:rstudio
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filter
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library(purrr) # funcion map
library(plm) # modelos lineales para datos panel
Attaching package: ‘plm’
The following objects are masked from ‘package:dplyr’:
between, lag, lead
library(car) # test y utilidades para modelos
Loading required package: carData
Attaching package: ‘car’
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recode
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library(htmltools) # para imprimir graficos en html
library(urca) # Unit root test
Datos para paises bajos ingresos sean utilizados, segun clasificación del banco mundial, hay 26 paises de bajos ingresos y 51 de ingresos medios bajos
country_class <- read_excel("CLASS.xlsx")
country_class %>%
filter(!is.na(Region), !is.na(`Income group`)) %>%
group_by(`Income group`) %>%
summarise(countries = n()) %>%
arrange(factor(`Income group`, levels = c('High income', 'Upper middle income', 'Lower middle income', 'Low income')))
Listado de paises a analisar:
my_countries <- country_class %>%
filter(!is.na(Region), `Income group` %in% c('Low income', 'Lower middle income')) %>%
select(Code)
my_countries %>% merge(country_class) %>% select(Code, Economy)
Hacer la respectiva asociacion de nombres iso3c e iso2c
my_countries$iso2c <- WDI_data$country %>%
filter(iso3c %in% my_countries$Code) %>%
.$iso2c
my_countries
Datos del banco mundial (para ODA y los indices de gobernanza) y el Human Development Reports API son descargados desde scripts de Python. Son almacenados en archivos CSV y luego son cargados aqui:
datos_HDI <- read_csv("datos_python_HDI.csv", col_names = c('Code', 'iso2c', 'indicator', 'year', 'value'),
col_types = list(col_character(), col_character(), col_character(), col_double(), col_double()))
hdi_indicators <- datos_HDI %>% distinct(indicator) %>% .$indicator
oda_indicators <- c(
'DT_ODA_ALLD_CD',
'DT_ODA_ALLD_KD',
'DT_ODA_OATL_CD',
'DT_ODA_OATL_KD',
'DT_ODA_ODAT_CD',
'DT_ODA_ODAT_GI_ZS',
'DT_ODA_ODAT_GN_ZS',
'DT_ODA_ODAT_KD',
'DT_ODA_ODAT_MP_ZS',
'DT_ODA_ODAT_PC_ZS',
'DT_ODA_ODAT_XP_ZS'
)
gob_indicators <- c(
'CC_EST',
'CC_NO_SRC',
'CC_PER_RNK',
'CC_PER_RNK_LOWER',
'CC_PER_RNK_UPPER',
'CC_STD_ERR',
'GE_EST',
'GE_NO_SRC',
'GE_PER_RNK',
'GE_PER_RNK_LOWER',
'GE_PER_RNK_UPPER',
'GE_STD_ERR',
'PV_EST',
'PV_NO_SRC',
'PV_PER_RNK',
'PV_PER_RNK_LOWER',
'PV_PER_RNK_UPPER',
'PV_STD_ERR',
'RQ_EST',
'RQ_NO_SRC',
'RQ_PER_RNK',
'RQ_PER_RNK_LOWER',
'RQ_PER_RNK_UPPER',
'RQ_STD_ERR',
'RL_EST',
'RL_NO_SRC',
'RL_PER_RNK',
'RL_PER_RNK_LOWER',
'RL_PER_RNK_UPPER',
'RL_STD_ERR',
'VA_EST',
'VA_NO_SRC',
'VA_PER_RNK',
'VA_PER_RNK_LOWER',
'VA_PER_RNK_UPPER',
'VA_STD_ERR'
)
gdp_indicators <- c(
'NY_ADJ_NNTY_PC_CD',
'NY_ADJ_NNTY_PC_KD',
'NY_ADJ_NNTY_PC_KD_ZG',
'NY_GDP_PCAP_CN',
'NY_GDP_PCAP_KN',
'NY_GDP_PCAP_CD',
'NY_GDP_PCAP_KD',
'NY_GDP_MKTP_KD_ZG',
'NY_GDP_DEFL_ZS_AD',
'NY_GDP_DEFL_ZS',
'NY_GDP_MKTP_CD',
'NY_GDP_MKTP_CN',
'NY_GDP_MKTP_KN',
'NY_GDP_MKTP_KD',
'NY_GDP_PCAP_KD_ZG',
'NY_GDP_PCAP_PP_KD',
'NY_GDP_PCAP_PP_CD',
'SL_GDP_PCAP_EM_KD',
'SP_POP_GROW'
)
datos_WB <- data.frame(indicator = character(), iso2c = character(), year = double(), value = double())
suppressWarnings(
for (indicator in c(oda_indicators, gob_indicators, gdp_indicators)) {
datos_WB <- rbind(datos_WB, read_csv(paste("datos_python", indicator, ".csv", sep =''),
col_names = c('indicator', 'iso2c', 'year', 'value'),
col_types = list(col_character(), col_character(), col_double(), col_double())))
}
)
Poverty <- read_excel("GlobalExtremePovertyDollaraDay_Compact.xlsx", sheet = "Data Long Format")
names(Poverty) <- c("ccode", "country", "year", "value")
Poverty[Poverty=="Cape Verde"] <- "Cabo Verde"
Poverty[Poverty=="Congo"] <- "Congo, Rep."
Poverty[Poverty=="Egypt"] <- "Egypt, Arab Rep."
Poverty[Poverty=="Iran"] <- "Iran, Islamic Rep."
Poverty[Poverty=="Kyrgyzstan"] <- "Kyrgyz Republic"
Poverty[Poverty=="Laos"] <- "Lao PDR"
Poverty[Poverty=="Macedonia"] <- "North Macedonia"
Poverty[Poverty=="Russia"] <- "Russian Federation"
Poverty[Poverty=="Slovakia"] <- "Slovak Republic"
Poverty[Poverty=="South Korea"] <- "Korea, Rep."
Poverty[Poverty=="Swaziland"] <- "Eswatini"
Poverty[Poverty=="Syria"] <- "Syrian Arab Republic"
Poverty[Poverty=="The Gambia"] <- "Gambia, The"
Poverty[Poverty=="Turkey"] <- "Turkiye"
Poverty[Poverty=="Venezuela"] <- "Venezuela, RB"
Poverty[Poverty=="Yemen"] <- "Yemen, Rep."
Poverty <- Poverty %>%
filter(year > 1994) %>%
merge(WDI_data$country, all.x = TRUE) %>%
mutate(indicator = 'POV') %>%
merge(my_countries) %>%
select(indicator, iso2c, year, value)
PC_LIB <- read_csv("political-civil-liberties-index.csv")
Rows: 33643 Columns: 4── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Entity, Code
dbl (2): year, value
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
PC_LIB <- PC_LIB %>%
filter(year > 1994, !is.na(Code)) %>%
merge(my_countries) %>%
mutate(indicator = 'POL.CIV.LIB') %>%
select(indicator, iso2c, year, value)
datos_paper <- rbind(datos_WB, datos_HDI %>% select(indicator, iso2c, year, value), Poverty, PC_LIB) %>%
pivot_wider(names_from = indicator, values_from = value)
datos_paper <- datos_paper %>% mutate(GOV = (CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST) / 6)
datos_paper <- datos_paper %>% arrange(iso2c, year) %>%
mutate(hdi_diff = case_when(iso2c == dplyr::lag(iso2c) ~ hdi - dplyr::lag(hdi), TRUE ~ NA_real_),
NY.GDP.PCAP.CD_diff = case_when(iso2c == dplyr::lag(iso2c) ~ NY.GDP.PCAP.CD - dplyr::lag(NY.GDP.PCAP.CD), TRUE ~ NA_real_),
DT.ODA.ALLD.CD_diff = case_when(iso2c == dplyr::lag(iso2c) ~ DT.ODA.ALLD.CD - dplyr::lag(DT.ODA.ALLD.CD), TRUE ~ NA_real_),
DT.ODA.ODAT.PC.ZS_diff = case_when(iso2c == dplyr::lag(iso2c) ~ DT.ODA.ODAT.PC.ZS - dplyr::lag(DT.ODA.ODAT.PC.ZS), TRUE ~ NA_real_),
GOV_diff = case_when(iso2c == dplyr::lag(iso2c) ~ GOV - dplyr::lag(GOV), TRUE ~ NA_real_),
POV_diff = case_when(iso2c == dplyr::lag(iso2c) ~ POV - dplyr::lag(POV), TRUE ~ NA_real_))
datos_paper <- datos_paper %>% mutate(GOV_GOOD = case_when(GOV >= 0 ~ 1, TRUE ~ 0))
plot_ly(data = datos_paper %>% filter(!is.na(GOV)), y = ~ GOV, type = 'scatter', mode = 'markers') %>%
layout(title = 'Indice promedio de gobernanza', xaxis = list(title = 'Registros'))
datos_paper <- datos_paper %>% mutate(POL.CIV.LIB_GOOD = case_when(POL.CIV.LIB >= 0.5 ~ 1, TRUE ~ 0))
plot_ly(data = datos_paper %>% filter(!is.na(POL.CIV.LIB)), y = ~ POL.CIV.LIB, type = 'scatter', mode = 'markers') %>%
layout(title = 'Indice libertades politicas y civiles', xaxis = list(title = 'Registros'))
datos_paper <- datos_paper %>% mutate(DT.ODA.ALLD.CD_LOG = log(DT.ODA.ALLD.CD))
Warning: NaNs produced
datos_paper <- datos_paper %>% mutate(DT.ODA.ODAT.PC.ZS_2 = DT.ODA.ODAT.PC.ZS ^ 2,
DT.ODA.ALLD.CD_2 = DT.ODA.ALLD.CD ^ 2,
DT.ODA.ALLD.CD_LOG_2 = DT.ODA.ALLD.CD_LOG ^ 2,)
vis_dat(datos_paper %>% select(all_of(gsub("_", ".", oda_indicators))))
# DT.ODA.OATL.CD and DT.ODA.OATL.KD faltan
# DT.ODA.ODAT.GI.ZS, DT.ODA.ODAT.GN.ZS, DT.ODA.ODAT.MP.ZS and DT.ODA.ODAT.XP.ZS tienen faltas
# Un par de ocurrencias pais-año que faltan datos
vis_dat(datos_paper %>% select(NY.GDP.PCAP.CN, NY.GDP.PCAP.CD))
# NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, NY.GDP.MKTP.CD, NY.GDP.MKTP.CN son buenos candidatos para usar como variables,
# 'SY'falta PIB per Capita en 2022, 2023 sin datos algunos paises
vis_dat(datos_paper %>% arrange(year) %>% select(all_of(gsub("_", ".", gob_indicators))))
# Datos del 2000 para atras tienen espacios faltantes
vis_dat(datos_paper %>% select(all_of(hdi_indicators)))
# abr, co2_prod, le, le_f, le_m, mmr son las pocas categorias sin datos faltantes
# hdi faltante en multiples ocaciones
vis_dat(datos_paper %>% arrange(iso2c) %>% select(SP.POP.GROW))
# ZW no tiene datos de crecimiento poblacional
vis_dat(datos_paper %>% arrange(year, iso2c) %>% select(POV))
# 'AF', 'CD', 'CI', 'DJ', 'KH', 'LR', 'MR', 'PG', 'ST', 'TJ', 'UZ', 'VN', 'WS' no tienen datos de esta variable
# Porcentaje de personas por debajo de la linea de extrema pobreza (Dollar a day)
vis_dat(datos_paper %>% arrange(iso2c) %>% select(POL.CIV.LIB))
# KI MR SD WS son paises sin datos para estos años
temp <- datos_paper %>%
filter(!year < 2002, !year > 2022, !iso2c %in% c('SS')) %>%
merge(my_countries) %>%
merge(country_class) %>%
group_by(`Income group`) %>%
summarise('2002 - 2022' = sum(DT.ODA.ALLD.CD))
temp
plot_ly(temp %>%
mutate(`Income group` = case_when(`Income group` == 'Low income' ~ 'Ingresos Bajos',
`Income group` == 'Lower middle income' ~ 'Ingresos Medios-bajos')),
labels = ~`Income group`, values = ~`2002 - 2022`, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Asistencia Oficial para el Desarrollo recibida total 2002 - 2022',
plot_bgcolor = "#e5ecf6")
########################################################################
temp <- datos_paper %>%
mutate(y_range = case_when(year < 2007 ~ '2002 - 2006',
year < 2012 ~ '2007 - 2011',
year < 2017 ~ '2012 - 2016',
TRUE ~ '2017 - 2022')) %>%
filter(!year < 2002, !year > 2022, !iso2c %in% c('SS')) %>%
merge(my_countries) %>%
merge(country_class) %>%
group_by(`Income group`, y_range) %>%
summarise(sum=sum(DT.ODA.ALLD.CD), .groups = "drop")
temp %>% pivot_wider(names_from = y_range, values_from = sum)
plot_ly(temp %>% pivot_wider(names_from = `Income group`, values_from = sum),
x = ~y_range, y = ~`Lower middle income`, name = 'Ingresos Medios-bajos', type = 'bar') %>%
add_trace(y = ~`Low income`, name = 'Ingresos Bajos') %>%
layout(title = 'Asistencia Oficial para el Desarrollo recibida total',
yaxis = list(title = 'Dolares Actuales'), xaxis = list(title = 'Periodo'), barmode = 'group')
########################################################################
temp <- datos_paper %>%
filter(!year < 2002, !year > 2022, !iso2c %in% c('SS')) %>%
merge(my_countries) %>%
merge(country_class) %>%
group_by(Region) %>%
summarise('2002 - 2022' = sum(DT.ODA.ALLD.CD))
temp
plot_ly(temp, labels = ~Region, values = ~`2002 - 2022`, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Asistencia Oficial para el Desarrollo recibida total 2002 - 2022',
plot_bgcolor = "#e5ecf6")
########################################################################
temp <- datos_paper %>%
mutate(y_range = case_when(year < 2007 ~ '2002 - 2006',
year < 2012 ~ '2007 - 2011',
year < 2017 ~ '2012 - 2016',
TRUE ~ '2017 - 2022')) %>%
filter(!year < 2002, !year > 2022, !iso2c %in% c('SS')) %>%
merge(my_countries) %>%
merge(country_class) %>%
group_by(Region, y_range) %>%
summarise(sum=sum(DT.ODA.ALLD.CD), .groups = "drop")
temp %>%
pivot_wider(names_from = y_range, values_from = sum)
plot_ly(temp %>% pivot_wider(names_from = Region, values_from = sum),
x = ~y_range, y = ~`Sub-Saharan Africa`, name = 'Sub-Saharan Africa', type = 'bar') %>%
add_trace(y = ~`East Asia & Pacific`, name = 'East Asia & Pacific') %>%
add_trace(y = ~`Middle East & North Africa`, name = 'Middle East & North Africa') %>%
add_trace(y = ~`South Asia`, name = 'South Asia') %>%
add_trace(y = ~`Latin America & Caribbean`, name = 'Latin America & Caribbean') %>%
add_trace(y = ~`Europe & Central Asia`, name = 'Europe & Central Asia') %>%
layout(title = 'Asistencia Oficial para el Desarrollo recibida total',
yaxis = list(title = 'Dolares Actuales'), xaxis = list(title = 'Periodo'), barmode = 'group')
########################################################################
temp <- datos_paper %>%
filter(!year < 2002, !year > 2022, !iso2c %in% c('SS', 'KP')) %>%
merge(my_countries) %>%
merge(country_class)
suppressWarnings(
plot_ly(temp %>% filter(year %in% c(2002, 2012, 2022)), x = ~year, y = ~hdi, color = ~`Income group`, type = "box") %>%
layout(title = 'Indice de desarrollo Humano en paises de Ingresos Bajos e Ingresos Medios-bajos',
boxmode = "group")
)
Warning: Ignoring 9 observationsWarning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
Warning: Ignoring 9 observationsWarning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
suppressWarnings(
plot_ly(temp %>% filter(year %in% c(2002, 2012, 2022)), x = ~year, y = ~hdi, color = ~Region, type = "box") %>%
layout(title = 'Indice de desarrollo Humano segun Region', boxmode = "group")
)
Warning: Ignoring 9 observationsWarning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
Warning: Ignoring 9 observationsWarning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
########################################################################
suppressWarnings(
plot_ly(temp %>% filter(year %in% c(2002, 2012, 2022)), x = ~year, y = ~GOV, color = ~`Income group`, type = "box") %>%
layout(title = 'Indice de gobernanza en paises de Ingresos Bajos e Ingresos Medios-bajos', boxmode = "group")
)
Warning: Ignoring 4 observationsWarning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
Warning: Ignoring 4 observationsWarning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: minimal value for n is 3, returning requested palette with 3 different levels
Warning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
suppressWarnings(
plot_ly(temp %>% filter(year %in% c(2002, 2012, 2022)), x = ~year, y = ~GOV, color = ~Region, type = "box") %>%
layout(title = 'Indice de gobernanza segun Region', boxmode = "group")
)
Warning: Ignoring 4 observationsWarning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
Warning: Ignoring 4 observationsWarning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
# variables de etiqueta
ve <- c('iso2c', 'year')
# variables depndientes
vd <- c('NY.GDP.PCAP.CD')
# 'hdi', 'hdi_diff', 'NY.GDP.PCAP.CD', 'NY.GDP.PCAP.CD_diff', 'POV', 'POV_diff',
# variables independientes
vi <- c('DT.ODA.ODAT.PC.ZS')
# 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff',
# 'DT.ODA.ALLD.CD:LOG'
# variables de control
vc <- c('GOV')
# 'SP.POP.GROW', 'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST', 'GOV', 'GOV_diff'
# 'NY.GDP.PCAP.CD', 'POL.CIV.LIB', 'DT.ODA.ODAT.PC.ZS_2', 'DT.ODA.ALLD.CD_2', 'DT.ODA.ALLD.CD_LOG_2'
# variables interactivas
vint <- c('GOV_GOOD') # 'GOV_GOOD', 'POL.CIV.LIB_GOOD'
# paises sin datos
delete_c <- c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB', 'SY')
#, 'KI', 'MR', 'SD', 'WS' Si se usa POL.CIV.LIB
#, 'AF', 'CD', 'CI', 'DJ', 'KH', 'LR', 'MR', 'PG', 'ST', 'TJ', 'UZ', 'VN', 'WS' Si se usa POV
#, 'LK', 'PH' Si se usa DT.ODA.ALLD.CD_LOG
# años sin datos
first_y <- 2002
last_y <- 2022 # 2018 si se usa POV
f <- paste(vd, '~', case_when(length(vint) > 0 ~ paste(vi, vint, sep = '*'), TRUE ~ vi), '+', paste(vc, collapse = ' + '))
datos_model <- datos_paper %>%
filter(!iso2c %in% delete_c, !year < first_y, !year > last_y) %>%
select(all_of(c(ve, vd, vi, vc, vint)))
datos_model
vis_dat(datos_model)
Se revisara las relaciones entre las variables graficamente
my_plot = list()
for (vd_ in vd) {
for (vi_ in c(vi, vc)){
fit <- lm(paste(vd_, '~', vi_) ,data = datos_model)
my_plot[[paste(vd_,vi_)]] <- plot_ly(x = datos_model[[vi_]],
y = datos_model[[vd_]],
type = 'scatter',
mode = 'markers',
name = vi_) %>%
add_lines(x = datos_model[[vi_]], fitted(fit), name = paste("trace", vi_))
}
}
subplot(my_plot, nrows = 2, margin = 0.05) %>% layout(title = vd)
NA
model_ols <- lm(f, data=datos_model)
model_fe <- plm(f, data=datos_model, index = ve, model = "within")
model_re <- plm(f, data=datos_model, index = ve, model = "random")
print(f)
[1] "NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS*GOV_GOOD + GOV"
summary(model_ols)
Call:
lm(formula = f, data = datos_model)
Residuals:
Min 1Q Median 3Q Max
-1482.5 -700.0 -259.1 464.9 4007.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1860.2240 67.1552 27.700 < 2e-16 ***
DT.ODA.ODAT.PC.ZS 0.9891 0.5228 1.892 0.058727 .
GOV_GOOD -559.5165 243.5629 -2.297 0.021770 *
GOV 764.0905 64.2218 11.898 < 2e-16 ***
DT.ODA.ODAT.PC.ZS:GOV_GOOD 2.9269 0.8561 3.419 0.000649 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 951.2 on 1255 degrees of freedom
Multiple R-squared: 0.1719, Adjusted R-squared: 0.1693
F-statistic: 65.15 on 4 and 1255 DF, p-value: < 2.2e-16
residualPlots(model_ols)
Test stat Pr(>|Test stat|)
DT.ODA.ODAT.PC.ZS 1.7740 0.0763 .
GOV_GOOD -0.5798 0.5622
GOV 0.6943 0.4877
Tukey test 0.3285 0.7425
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot(model_ols)
vif(model_ols)
DT.ODA.ODAT.PC.ZS GOV_GOOD GOV DT.ODA.ODAT.PC.ZS:GOV_GOOD
2.077116 2.539253 1.218789 3.731455
print(f)
[1] "NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS*GOV_GOOD + GOV"
summary(model_fe)
Oneway (individual) effect Within Model
Call:
plm(formula = f, data = datos_model, model = "within", index = ve)
Balanced Panel: n = 60, T = 21, N = 1260
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-2058.614 -215.417 37.836 225.491 2234.837
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
DT.ODA.ODAT.PC.ZS 3.58439 0.48808 7.3439 3.827e-13 ***
GOV_GOOD -297.32060 172.98149 -1.7188 0.08591 .
GOV 462.66804 97.03124 4.7682 2.087e-06 ***
DT.ODA.ODAT.PC.ZS:GOV_GOOD -0.57121 0.77057 -0.7413 0.45867
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Total Sum of Squares: 377790000
Residual Sum of Squares: 347380000
R-Squared: 0.080495
Adj. R-Squared: 0.032059
F-statistic: 26.1749 on 4 and 1196 DF, p-value: < 2.22e-16
#summary(lm(paste(f, '+ iso2c'), data=datos_model))
print(f)
[1] "NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS*GOV_GOOD + GOV"
summary(model_re)
Oneway (individual) effect Random Effect Model
(Swamy-Arora's transformation)
Call:
plm(formula = f, data = datos_model, model = "random", index = ve)
Balanced Panel: n = 60, T = 21, N = 1260
Effects:
var std.dev share
idiosyncratic 290449.0 538.9 0.308
individual 653014.1 808.1 0.692
theta: 0.856
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-1862.042 -256.189 -23.048 232.078 2495.802
Coefficients:
Estimate Std. Error z-value Pr(>|z|)
(Intercept) 1525.21846 129.37534 11.7891 < 2.2e-16 ***
DT.ODA.ODAT.PC.ZS 3.46478 0.47923 7.2298 4.836e-13 ***
GOV_GOOD -303.98193 171.33604 -1.7742 0.07603 .
GOV 506.96342 90.67700 5.5909 2.259e-08 ***
DT.ODA.ODAT.PC.ZS:GOV_GOOD -0.33860 0.75459 -0.4487 0.65363
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Total Sum of Squares: 398390000
Residual Sum of Squares: 364620000
R-Squared: 0.084765
Adj. R-Squared: 0.081848
Chisq: 116.232 on 4 DF, p-value: < 2.22e-16
print(f)
[1] "NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS*GOV_GOOD + GOV"
phtest(model_fe, model_re)
Hausman Test
data: f
chisq = 4.4101, df = 4, p-value = 0.3533
alternative hypothesis: one model is inconsistent
df=ur.df(datos_model %>% filter(iso2c == 'AO') %>% .[[vi]],type="none",lags=0)
df
###############################################################
# Augmented Dickey-Fuller Test Unit Root / Cointegration Test #
###############################################################
The value of the test statistic is: -1.8892
print('La serie es estacionaria si el valor del estadistico de prueba excede estos valores al 99%, 95% y 90% respectivamente')
[1] "La serie es estacionaria si el valor del estadistico de prueba excede estos valores al 99%, 95% y 90% respectivamente"
qnorm(c(.01,.05,.1)/2)
[1] -2.575829 -1.959964 -1.644854
adf.test(datos_model[[vi]], k= 0)
Warning: p-value smaller than printed p-value
Augmented Dickey-Fuller Test
data: datos_model[[vi]]
Dickey-Fuller = -10.821, Lag order = 0, p-value = 0.01
alternative hypothesis: stationary
y <- data.frame(split(datos_model[[vi]], datos_model$iso2c)) # individuals in columns
purtest(y, pmax = 4, exo = "intercept", test = "madwu")
Maddala-Wu Unit-Root Test (ex. var.: Individual Intercepts)
data: y
chisq = 351.26, df = 120, p-value < 2.2e-16
alternative hypothesis: stationarity
pcdtest(model_fe, test = c("lm"))
Breusch-Pagan LM test for cross-sectional dependence in panels
data: NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS * GOV_GOOD + GOV
chisq = 16091, df = 1770, p-value < 2.2e-16
alternative hypothesis: cross-sectional dependence
pcdtest(model_fe, test = c("cd"))
Pesaran CD test for cross-sectional dependence in panels
data: NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS * GOV_GOOD + GOV
z = 106.09, p-value < 2.2e-16
alternative hypothesis: cross-sectional dependence
pbgtest(model_fe)
Breusch-Godfrey/Wooldridge test for serial correlation in panel models
data: f
chisq = 801.59, df = 21, p-value < 2.2e-16
alternative hypothesis: serial correlation in idiosyncratic errors
bptest(hdi ~ DT.ODA.ODAT.PC.ZS*GOV_GOOD + NY.GDP.PCAP.CD + GOV + SP.POP.GROW, data = datos_model, studentize = F)
Error in bptest(hdi ~ DT.ODA.ODAT.PC.ZS * GOV_GOOD + NY.GDP.PCAP.CD + :
could not find function "bptest"
coeftest(model_fe)
Error in coeftest(model_fe) : could not find function "coeftest"
save(f, delete_c, first_y, last_y, my_plot, model_ols, model_fe, model_re, file = "GDPPC_ODAPCGOVGOOD_GOV.RData")